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Sparse Principal Component Analysis (SPCA) is a method that can get the sparse loadings of the principal components (PCs), and it may formulate PCA as a regression-type optimization problem by using the elastic net. But the selected features are different with each PC and generally independent. A new method named SPCA has been proposed for removing these detect, which replaces the elastic net with...
Many methods have been considered for gene selection and analysis of gene expression data. Nonetheless, there still exists the considerable space for improving the explicitness and reliability of gene selection. To this end, this paper proposes a novel method named robust graph regularized non-negative matrix factorization for characteristic gene selection using gene expression data, which mainly...
The significant advantage of sparse methods is to reduce the complicacy of genes expression data, which makes them easier to understand and interpret. In this paper, we propose a novel Class-information-based Sparse Non-negative Matrix Factorization (CISNMF) method which introduces the class information by the total scatter matrix. Firstly, the total scatter matrix is obtained via combining the between-class...
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